The Xiamen Symposium on Marine Environmental Sciences

2027 Xiamen Symposium on Marine Environmental Sciences

January 12-15, 2027

A Coupled Statistical Learning and Machine Learning Framework for Global Daily Seamless Secchi Disk Depth Reconstruction (1998-2023)
ID:1137 Oral Presentation

2027-01-13 16:35 (China Standard Time)

Session:Session 64 - Advancing Climate Data Records: Creation and Application of Long-term Ocean Color Products

Abstract
Secchi Disk Depth (ZSD) is a crucial indicator characterizing marine ecological environment, playing a key role in monitoring water clarity and understanding climate-driven processes. Despite recent advances in satellite remote sensing retrieval of ZSD, existing products remain constrained by deficiencies in reliability, temporal continuity, and spatial completeness in coastal waters. To address these limitations, we propose a coupled reconstruction framework (DCT-XGB) to generate a global, daily, high-accuracy, and seamless ZSD dataset (65°N-65°S, 4 km resolution) from multi-satellite merged OC-CCI remote sensing reflectance data (1998–2023). Building upon reliable retrieval results (APDm = 28.9%), we developed the DCT-XGB model (APDm = 23.5%) to reconstruct missing regions by integrating traditional statistical learning (Discrete Cosine Transform-Penalized Least Squares, DCT-PLS) and machine learning (Extreme Gradient Boosting, XGBoost). Specifically, it fuses the DCT-PLS-derived ZSD background field with environmental and temporal drivers (Mixed Layer Depth, Sea Surface Temperature, Month, Day of Year) to produce the final gap-free product. Independent evaluation with in situ observation reveals that this product has substantially higher overall accuracy (APDm = 27.5%) than the existing GlobColour product (APDm = 56.1%). Notably, in low-ZSD waters (ZSD≤5m), the new product improves accuracy by 37%. Furthermore, the new product reliably reproduces the time-series characteristics of in situ ZSD at validation stations, with higher correlation (R2 = 0.59±0.15) and lower error (APDm = 16.3%±5.3%) compared to GlobColour (R2 = 0.22±0.19, APDm = 36.0%±16.6%). Trend analysis based on the new product reveals a significant contracting trend (–1.9×10⁴ km² yr⁻¹, P < 0.01) in low-ZSD coastal waters across the global ocean over the past 26 years, a feature that cannot be captured by the GlobColour product due to its limited accuracy.  In summary, the proposed framework and the resulting global daily ZSD product represent a significant breakthrough in spatial completeness, temporal continuity, and coastal accuracy. By effectively overcoming the spatiotemporal discontinuities of existing satellite records, this work provides high-quality, long-term data essential for marine ecological monitoring and global climate change research.
 
Speaker
Jinzhao Xiang Sun Yat-sen University

Author
Xiang Jinzhao Sun Yat-sen University

    Important Dates

    • Jan 30

      2026

      Session / Event proposals open

    • Mar 31

      2026

      Session / Event proposals close
      (Extended to April 7)

    • Apr 30

      2026

      Session acceptance notifications

    • Apr 30

      2026

      Abstract submissions & registration open

    • Jun 30

      2026

      Abstract submissions close
      (Extended to July 21)

    • Aug 31

      2026

      Abstract acceptance notifications

    • Sep 15

      2026

      Scientific program released

    • Oct 15

      2026

      Early bird registration closes

    • Jan 12–15

      2027

      XMAS 2027